A deep learning model for syncope recognition in the emergency department

Abstract Introduction Syncope is a common presenting symptom in the emergency departments (EDs) and poses challenges in its recognition and differentiation from alternative causes of transient loss of consciousness. Natural Language Processing (NLP) is an artificial intelligence technique that enables the analysis of natural language, such as clinical notes and medical history within electronic medical records (EMRs). The application of NLP into the medical field is surging but its use for the recognition of syncope is still underexplored. Purpose We aimed to develop a NLP-based deep learning model for syncope recognition in the ED. Methods Two models, based on the Italian and Multilingual Bidirectional Encoder Representations from Transformers (BERT) language model, were developed and evaluated using consecutive EMRs from Humanitas Research Hospital ED. The first "triage" model was only based on textual data contained in the "triage" section of the EMR. The second "anamnesis" model also considered textual data contained in the "medical history" section. Performance metrics including area under the curve (AUC), Matthew’s correlation coefficient (MCC) and Brier’s score (BS) were used to compare the discriminative capabilities of different BERT models. Results The models were developed and tested on 15,098 and 15,222 EMRs, respectively. The Multilanguage BERT discriminated patients with syncope with an AUC of 0.85 in the triage model and of 0.92 in the anamnesis model. The Italian BERT identified patients with syncope with a MCC of 0.50 and a BS of 0.02 in the triage model and of 0.64 and 0.01, respectively, in the anamnesis model. Conclusions We propose two NLP-based models that identify syncope patients with a high discriminative capability. The presented algorithm may provide a starting point for the management of syncope patients using machine learning techniques.ROC and calibration curves of modelsFramework for model implementation